Time dispersion analysis of features as a tool for investigating plant electrophysiology: A case study using moderate...

Electrophysiological signals in plants, which are a part of the plant electrome, are essential for mediating responses to environmental stimuli but exhibit complex, non-linear dynamics that challenge conventional analyses. Here, we introduce the time dispersion analysis of features (TDAF), a novel method that preserves temporal integrity by assessing the dispersion of signal features over time by segmenting time series and evaluating the temporal evolution of extracted features. Unlike traditional methods, such as moving averages or stationarity-based models, that summarize the signal or lose temporal information, TDAF analyzes the evolution of features over time, maintaining their dynamic structure. We applied TDAF to investigate the effects of a moderate static magnetic field (~ 0.4 mT) on the electrome of common bean plants (Phaseolus vulgaris L.). Signals from 30 plants were recorded before and during magnetic field exposure, generating time series with 225,000 points each. Features such as approximate entropy (ApEn), detrended fluctuation analysis (DFA), fast Fourier transform (FFT), power spectral density (PSD), and average band power (ABP) were analyzed. Our results suggest that magnetic field exposure tends to reduce signal amplitude but preserves the structural complexity and temporal patterns of the electrome, indicating modulation without loss of information processing capacity. TDAF proved effective for detecting subtle physiological changes and offers a valuable tool for advancing plant electrophysiology, bioelectromagnetic research, and studies involving complex and long-duration biological signals.